Autonomous Farming Robots for Real-Time Weed Detection and Removal using YOLOv8

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The increasing demand for sustainable agriculture has driven the development of autonomous farming solutions for precise weed management. Traditional weed control methods, including manual removal and chemical herbicides, are labor-intensive, environmentally harmful, and economically inefficient. This study proposes an autonomous farming robot equipped with YOLOv8 (You Only Look Once, version 8) for real-time weed detection and removal. The system integrates high-resolution cameras, deep learning-based image processing, and a robotic arm with an adaptive end-effector to eliminate weeds efficiently. The YOLOv8 model, trained on a dataset of 50,000 images, achieved an mAP@50 of 92.4%, demonstrating superior performance compared to existing state-of-the-art detection models. The robot, tested across various crop fields, achieved an average weed removal accuracy of 89.7%, reducing herbicide usage by 67% while increasing yield potential by 15%. Compared to manual weeding, the system improved operational efficiency by 58%. These findings highlight the potential of AI-driven robotic systems in enhancing agricultural productivity, minimizing chemical dependency, and promoting eco-friendly farming practices. Future work will focus on multi-weed classification, real-time adaptation to diverse field conditions, and energy-efficient navigation to further optimize performance.